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Record W2345607311 · doi:10.21273/hortsci.48.4.510

Compost Teas and Reused Nutrient Solution Suppress Plant Pathogens In Vitro

2013· article· en· W2345607311 on OpenAlexafffund
Xiuling Tian, Youbin Zheng

Bibliographic record

VenueHortScience · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsUniversity of GuelphVineland Research and Innovation Centre
FundersAgriculture and Agri-Food CanadaOntario Centres of Excellence
KeywordsSclerotinia sclerotiorumCompostRhizoctonia solaniBiologyFusarium solaniHorticultureBark (sound)Pythium ultimumNutrientFusarium oxysporumBotanyAgronomy

Abstract

fetched live from OpenAlex

In vitro testing was conducted to evaluate the inhibition potential of three compost teas (pine bark, manure, and vermicasting), Root Rescue Landscape Powder® (a mix of mycorrhizae and other beneficial microbes), waste diatomaceous earth (DE; from beer brewing), and a greenhouse nutrient solution, which had been reused for 20 years on six plant pathogens: Fusarium foetens , Rhizoctonia solani , Sclerotinia sclerotiorum , Phytophthora cryptogea , Pythium intermedium, and P. ultimum . The test materials showed in vitro inhibition on most of the test pathogens. Pine bark tea suppressed growth of all six pathogens, and inhibition exceeded 50% after 10 days of coincubation. Vermicasting tea showed over 40% inhibition against S. sclerotiorum and F. foetens ; manure tea showed 42% inhibition against F. foetens ; DE showed 40% inhibition against F. foetens , S. sclerotiorum , and R. solani ; whereas reused greenhouse nutrient solution showed 56.7% inhibition against R. solani and 43.4% inhibition against F. foetens ; Root Rescue showed 66% inhibition against P. intermedium. The results suggest that the six test materials have potential in the control of these soil- and water-borne pathogens in plant production system.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score0.314

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.202
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations18
Published2013
Admission routes2
Has abstractyes

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